Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region

Abstract

Mangrove forests play a global role in providing ecosystem goods and services in addition to acting as carbon sinks, and are particularly vulnerable to climate change effects such as rising sea levels and increased salinity. For this reason, accurate long-term monitoring of mangrove ecosystems is vital. However, these ecosystems are extremely dynamic and data frequency is often reduced by cloud cover. The Continuous Change Detection and Classification (CCDC) method has the potential to overcome this by utilising every available observation on a per-pixel basis to build stable season-trend models of the underlying phenology. These models can then be used for land cover classification and to determine greening and browning trends. To demonstrate the utility of this approach, CCDC was applied to a 30-year time series of Landsat data covering an area of mangrove forest known as the Sundarbans. Spanning the delta formed by the confluence of the Ganges, Brahmaputra and Meghna river systems, the Sundarbans is the largest contiguous mangrove forest in the world. CCDC achieved an overall classification accuracy of 94.5% with a 99% confidence of being between 94.2% and 94.8%. Results showed that while mangrove extent in the Sundarbans has remained stable, around 25% of the area experienced an overall negative trend, probably due to the effect of die-back on Heritiera fomes. In addition, dates and magnitudes of change derived from CCDC were used to investigate damage and recovery from a major cyclone; 11% of the Sundarbans was found to have been affected by Cyclone Sidr in 2007, 47.6% of which had not recovered by mid-2018. The results indicate that while the Sundarbans forest is resilient to cyclone events, the long-term degrading effects of climate change could reduce this resilience to critical levels. The proposed methodology, while computationally expensive, also offers means by which the full Landsat archive can be analyzed and interpreted and should be considered for global application to mangrove monitoring.

Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

“…The study from Zhu et al (2014) that first presented this approach by input all the Landsat bands to detect the various types of land cover change. Recently, many studies also demonstrated that the CCDC is an adaptive approach in that the single band, such as the NDVI or EVI, can be used to detect the land cover change for a single type [55]. The model of CCDC is shown in Equation (2):…”

Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data

“…We set the dynamics from the impervious surface as the urban expansion indicator. The ground truth samples were collected via visual interpretation, aided by very high resolution (VHR) images from Google Earth TM and prior research [22,75]. We randomly selected 26,670 samples within the study area among the six land cover categories; the samples proportion for each LULC category was controlled by the percentage of each class area in FROM-GLC10 (2017) [54,76].…”

Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification

“…This process therefore allows for highly accurate but time-limited data sets such as the GMW to easily be extrapolated through time. In a previous study, we demonstrated that the CCDC method trained using GMW data could produce highly accurate maps of mangrove extent over the Sundarbans mangrove forest, in addition to tracking changes in mangrove condition over 30 years [31].…”

“…This removes the requirement for finding cloud-free images for comparison and, since observations from different years are unlikely to fall on the same Day of Year (DOY), the effect of missing data is mitigated when applied to long time series. CCDC has previously been demonstrated to be an effective method for long-term mangrove classification and monitoring [31].…”